Learning cum Development Outcomes and Training Budget of Technology Based Entrepreneurial Firms in a Recessive Economy

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Covenant Journal of Entrepreneurship (CJoE) Vol. 3 No.1, June 2019

ISSN: p. 2682-5295 e. 2682-5309 DOI: 10.20370/rmj7-tf89 An Open Access Journal Available Online

Learning cum Development Outcomes and Training

Budget of Technology Based Entrepreneurial Firms in a

Recessive Economy

Akinbola Olufemi Amos

1

_{, Kowo Solomon Akpoviroro}

2

_,

Akinbola Omolola Sariat

2

_{& Badewole Oluwatimilehin Adesubomi}

1

1_{Department of Business Administration, College of Management Sciences.}

Federal University of Agriculture, Abeokuta akinbolaoa@funaab.edu.ng, badewoletimilehin@gmail.com

2_{Department of Business and Entrepreneurship, Kwara State University, Malete}

kowosolomon@gmail.com, omololakinbola@gmail.com

Received: 23.04.2019 Accepted: 30.05.2019 Date of Publication: June, 2019

Abstract: Learning and development outcomes in organizations have been of

contention in most technology based entrepreneurial firms in recessive economies like Nigeria and the inability to appropriate finance for learning and development priorities tend to inhibit the growth of human capital in the nation’s economy at large. The research analyzed the effect of operation budget on learning effectiveness during recession and evaluated the effect static budget on competitive advantage during recession. The findings showed that operation budget have significant effect on learning effectiveness (at P =0.004). It was also found that static budget does not have any significant relationship with competitive advantage (at P= 0.084). The research concludes that economic meltdown has not too many effects on learning and development outcomes of human capital as organizations still gets value for trainings on employee with reference to productivity in Nigeria. The study further recommends that entrepreneurial firms should create enabling operating environment for employees through right learning and development policies to avoid degradation of human capital.

Keywords: Learning Cum Development, Training Budget, Entrepreneurial firms

JEL CODES: M1, M19

Introduction

The corporate society has advanced more than ever as businesses are challenged with the hope of being

accountable for their personnel learning and development more than before, owing to several changes in the market environment because of the transient

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Akinbola O. Amos,et al CJoE (2019) 3(1) 20-35

URL http://journals.covenantuniversity.edu.ng/index.php/cjoe

adverse nature of the country.

Nevertheless, the receding nature of the economy is raising incredible interest about the potential drop in learning and development estimates (Vemic, 2010). It is always projected that the extent of investment in workforce learning and development lessen during the receding phase of a country as corporations look to reduce cost. Alternatively, while, the

organizational setting is being

significantly restructured; workers are projected to have a much array of

abilities, expertise and experience

(Brenner, 2011). Consequently, for every increment in expectation, the call for skillful and proficient personnel increases to help organization maintain its market allocation and extend competitive lead (Fitzroy and Hulbert, 2012).

Statement of the Research Problem

Recession in the economy has

significant effect on countries economic system. Learning and development experts are of the view that an important task now could be to set up the extent of the effect of economic meltdown on the learning and training of employees. Economic players are challenged with understanding of what ought to improve and enhance the development of

employees. Besides the receding

economic problem, there is an extended rate of joblessness along demographic lines. With the worldwide financial crisis and the increasing rate of

unemployment along demographic

lines, there's a challenge of what impact will the world economic downturn have on learning and development (Adamu, 2009) and (Ogbari, et al, 2017).

Learning and development professional are also involved on whether or not there may be any connection between economic recession and learning & development outcomes of employees. A variety of studies have been embarked upon relating to economic recession. Fewer of these, if any, have without a doubt endeavored to find out the effect of economic recession on learning and development outcomes in an emerging economy as that of Nigeria. With reference to these, the study intends to ascertain through the hypotheses as stated in null forms;

H01: There is no significant influence of

operation budget on learning

effectiveness.

H02: There is no significant relationship

between static budget and competitive advantage.

Concept of Learning and

Development in Economic Recession Recession is commonly depicted by a condition of undesirable economic

advancement consistent for two

successive financial periods. The Great Depression of 1930 became the worst financial crisis the world had witnessed before the global crisis of 2008 that didn’t exempt Nigeria and major entrepreneurial firms. (Pells, 2008). Learning, unlike training, is normally described, by way of training as well as education. (Jensen 2001). According to (Sloman 2005), learning can be defined

as ‘a self-ignited, job-centered

procedure leading to enhanced adaptive capability.’ ‘Learning’ is the wider blanket word through which both training and development are best comprehended. In essence learning and

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development goes concurrently and organizations tend to have different perception about dedicating funds for training needs of employees especially during recession as most organizations tend to cut operational cost. The essence

of training employees has been

adjudged to be of on necessity in trying times of firms because they do not take cognizance of the benefits trainings offer as a result of declining profit in recession.

Duggan (2017) pointed that

organizations budget also have a lot to do with the rate at which employees are trained and development outcomes tend to determine how much is dedicated to employee learning process. Training budgets normally describe how money

may be allocated for training,

development and delivery for an

organization. Funding a training

program calls for the evaluation of needs, making decisions and examining results. It was further pointed that organizations have categories of budgets ranging from operational to static budget in most organizations.

Inference to Human Capital Theory The study holds it footings on human capital theory which is amongst pioneering theories to account for human capital development especially as developing nations are concerned. This concept exemplifies the advantages of making an investment in learning and growth in relation to individual’s human capital. Investing in individuals has many benefits, it assists in increasing employers’ human resource personnel and help improve productivity (Becker,

1993). However, lack of skilled labor in developing countries has precipitated employers to invest more in their employees’ learning and development programs (Owoyemi et al., 2011). Empirical Framework

Several researches has been conducted on learning and development outcomes and their relations with training budget in a recessive economic system both in Nigeria and other economies of the world. However, in most researches performed, it has been validated that learning and development has only benefited little from training budget in a recessive economic system.

The countries of the world suffer from economic recession, nevertheless if the globe is receding or otherwise, or at the brink of downturn, is a topic a lot

argued. However, one thing

predominantly significant is that

organizations are reducing their budgets and hesitant to spend, and so are their work force. Perhaps, for most economic units that are trying to pull back on owns judgement on spending, generally, items to be rationed down is the estimates on training, learning and development programs. In response to recession, most organization intend to reduce learning and development budget

by 10% (Noe, 2002). However,

MacDuffie and Kochan (1995); Falola, et al (2017) argued that, in a recessive financial system, opportunities are open to companies and this include the identification of activities which might be crucial to commercial enterprise strategic growth. And those activities which might be mandated by way of

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regulation (such as sexual harassment and safety training). Nevertheless, learning and development needs in a recessive financial system relies on the needs of the organization. Although, many businesses do reduce their training budgets, they still sponsor programs which are especially critical in other to prepare for economic growth (Owens, 2006).

Methodology

The method adopted for the conduct of this research is the survey with insight to expo facto approach. Respondents’ opinion was gathered by administering structured questionnaire and the sample of the population of the study is based on complete enumeration of the employees of the technology based entrepreneurial firms since they have the indepth understanding and technical knowledge or non-conventional learning and the nature of sample is purposive. The sample consist of two hundred and eighty four (284) employees of 37 major technology based entrepreneurial firms including major phones imports and retail stores in Lagos state gotten from the Computer and Allied Product

Dealers Association of Nigeria

(CAPDAN) list and based on the criteria

that the firms reflected the

characteristics of investment in learning and development. Ethical issues in line with validity and reliability were considered to get accurate response and also protect the interest of the identity of business owners and employees. Also, the questionnaire was dispensed to two hundred and eighty-four personnel who was the sample size denoting the preferred population of the study of the

purposively chosen 37 technology based

entrepreneurial firms in Ikeja

(CAPDAN) section of Lagos state. Of this lot, one hundred and ninety-nine (199) questionnaires signifying 70% were returned, while eighty- five (85) questionnaires signifying 30% were not returned.

Data Presentation, Analysis and Discussion

The frequency distribution of the

respondents’ demographic

characteristics is presented in table 4.2 below. The table shows that out of the one hundred and ninety-nine (199) respondents, 135 (67.8%) are male, while 64 (32.2%) are female. We have more male respondents to female respondents in the sample. In addition, out of the one hundred and ninety-nine (199) respondents, 70 (35.2%) are single while 119 (59.8%) are married and 10 (5.0%) are neither married nor single. , most of the respondents are married. More so, 99 (49.7%) of the 199 respondents have 1-5 years’ work experience, 80 (40.2%) have 6-10 years’ work experience, 16 (8.0%) have 11-15 years’ work experience and, 4 (2.1%) have over 15years work experience. Most of the respondents have between 1-5years of work experience. Also, there are 44 M.SC and M.BA holders (22.1 per cent), 130 HND/BSc holders (65.3 per cent), 18 are SSCE holders (9.0 per cent), in the sample and 7 have other qualifications (3.6 per cent). The

respondents have high HND/BSc

educational qualifications. Again, out of the one hundred and ninety-nine (199) respondents, 6 (3.1%) are 51 years and above, 19 (9.5%) are between 41 and 50

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years of age, 98 (49.2%) are between 31 and 40 years, and 76 (38.2%) are between 21 and 30 years. most of the respondents are between the age of 31 and 40 years. More importantly, out of the 199 respondents, 6 (3.0%) are employees in the artisan industry; 69

(34.7%) are employees in the service industry; 101 (50.38%) are employees in manufacturing industry while 23 (11.5%) do not specify their industry. We have more of manufacturing industry employees as respondents in the sample.

Table 4.2: Frequency Distribution of the Respondents’ Demographic Characteristics

Characteristics Category Frequency Percentage Cumulative

percent

GENDER Male 135 67.8 67.8

Female 64 32.2 100.0

MARITAL STATUS Single 70 35.2 35.2

Married 119 59.8 95.0

Others 10 5.0 100.0

WORK EXPERIENCE 1-5 years 99 49.7 49.7

6-10 years 80 40.2 89.9 11-15 years 16 8.0 97.9 Over 15 years 4 2.1 100.0 INDUSTRY Manufacturing 101 50.8 50.8 Service 69 34.7 85.5 Artisan 6 3.0 88.5 Others 23 11.5 100.0 EDUCATIONAL QUALIFICATION SSCE 18 9.0 9.0 HND/BSC 130 65.3 74.4 MSC/MBA 44 22.1 96.5 Others 7 3.6 100.0 AGE 21-30 76 38.2 38.2 31-40 98 49.2 87.4 41-50 19 9.5 96.9 above 50 6 3.1 100.0

Source: Author’s Fieldwork Computation, 2018

Descriptive Statistics of the Respondents’ Perceptions

The descriptive statistics of the

respondents’ perceptions is presented in table 2 below. Concerning Operation Budget (OB), from 199 respondents; the range of (OB) is from 2 to 5 points, with a mean of 4.36 and standard deviation

of 0.40, the respondents, on average, strongly agreed with questions on (OB). Concerning Static Budget (SB), we have information from 199 respondents; the range of Static Budget (SB) is from 1 to 5 points, with a mean of 4.37 and

standard deviation of 0.52, the

respondents are, on average, strongly

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agreed with questions on Static Budget

(SB). Concerning Learning

Effectiveness, we have information from 199 respondents; the range of Learning Effectiveness is from 1 to 5 points, with a mean of 4.42 and standard deviation of 0.40, the respondents, on average, strongly agreed with questions

on Learning Effectiveness. Concerning Competitive Advantage (CA), we have information from 199 respondents; the range of (CA) is from 1 to 5 points, with a mean of 2.78 and standard deviation of 0.60, the respondents, on average, agreed with questions on Competitive Advantage.

Table 2: Descriptive Statistics

N Minimum Maximum Mean Std. Deviation

OPERATION BUDGET 199 2.00 5.00 4.3631 .39540

STATIC BUDGET 199 1.00 5.00 4.3756 .52216

LEARNING EFFECTIVENESS 199 1.00 5.00 4.4234 .38365

COMPETITIVE ADVANTAGE 199 1.00 5.00 2.7877 .56505

Valid N (list wise) 199

The hypotheses of the study are: (1) Operation Budget and Static Budget does not significantly affect Learning

Effectiveness; (2) There is no

significant relationship between

Operation Budget, and Static Budget on Competitive Advantage. To investigate these hypotheses and arrive at the objectives of the research, multiple regression analysis was used. Multiple regression is centered on correlation but permits a more advanced evaluation of the interrelationship amongst a set of variables. It creates a number of assumptions about the data which are

normality that believed that the

dependent variable is naturally

distributed (i.e. Learning and

Development Outcomes),

multicollinearity that believed that the

independent variables (Operation

Budget and Static Budget) are not well

interrelated, also Homoscedasticity

which believed that the variation amongst observations is equal and linearity which believed that the connection existing between dependent and independent variables is linear. Test of Normality

A normal curve can be portrayed to test for normality of the dependent variable

(i.e. Learning Effectiveness and

Competitive Advantage). Fig 1 to 2 presents a normal curve of Learning and Development Outcomes scores. Most of the parametric statistics presumes that the scores on each of the variables are naturally distributed (i.e. follow the shape of the normal curve). In this study, the scores are reasonably normally distributed, with most scores appearing in the Centre, narrowing out towards the edges.

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Fig 1: Histogram of Perceived Learning Effectiveness Scores

Fig 2: Histogram of Perceived Competitive Advantage scores

To check for multicollinearity, bivariate correlation was performed in Table 3 below. In the table, the highest correlation was 0.470. It shows little

multicollinearity problem among

Training Budget variables (Operation Budget and Static Budget). Thus, all the variables were maintained.

Table 4: Correlation among Training Budget Variables

OPERATING BUDGET CASHFLOW BUDGET STATIC BUDGET OPERATION BUDGET Pearson Correlation 1 .451

** _.438**

Sig. (2-tailed) .000 .000

N 199 199 199

STATIC BUDGET Pearson Correlation .438

** _.470** ₁

Sig. (2-tailed) .000 .000

N 199 199 199

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Test of Homoscedasticity and Linearity for Hypothesis one

A scatter plot was generated to test for homoscedasticity and linearity of the

relationship between dependent

variables (i.e. Learning Effectiveness

and Competitive Advantage) and

independent variables (i.e. Operation Budget and Static Budget). Fig. 3 and 4 depict the outcome of the scatter plots. From the outcome below, there shows to be a balanced, positive correlation among the variables.

Fig 3: Scatter Plot of Perceived Operation Budget and Learning Effectiveness Scores

Fig 4. Scatter Plot of Perceived Static Budget and Learning Effectiveness Scores

Test of Hypothesis One

Ho1: Operation Budget and Static

Budget do not significantly affect

Learning Effectiveness. Standard

multiple regression was used to discover the outcomes of Operation Budget and

Static Budget on Learning

Effectiveness. Initial analyses were

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done to make sure there is no violation of the assumptions of normality, Multicollinearity, homoscedasticity and linearity. The result of regression as

contained in Table 4, ANOVA, shows that the F-test was 14.853, significant at 5 percent [p<.000]. This showed that the model was well specified.

Table 4 ANOVAa Model Sum of Squares Df Mean Square F Sig. 1 Regression 5.421 3 1.807 14.853 .000b Residual 23.723 195 .122 Total 29.144 198

a. Dependent Variable: Learning Effectiveness b. Predictors: (Constant), Static Budget, Operation Budget

Source:

Author’s Fieldwork Computation, 2018

Also, the result of regression as contained in Table 5: Model Summary, shows that the R Square gave a large value of 18.6 per cent. This denotes that

the model (which includes Static Budget and Operation Budget) explained about 18.6 per cent of the variance in perceived Learning Effectiveness.

Table 5 Model Summary

Model R R Square Adjusted R

Square

Std. Error of the Estimate

1 .431a _.186 _.173 _.34879

a. Predictors: (Constant), Static Budget and Operation Budget

Source:

Author’s Fieldwork Computation, 2018

Particularly, the result of regression as contained in Table 6 Regression Coefficients, tests the first hypothesis of this study. From the output below, there

was positive relationship between

perceived Operation Budget and

perceived Learning Effectiveness such that a unit rise in Operation Budget scores caused about .212 unit increases in perceived Learning Effectiveness scores which was statistically significant at 5 per cent with the aid of the p value (0.004). Based on the result, the null

hypothesis is rejected; thus, there was positive relationship between Learning Effectiveness and Operation Budget. Additionally, there exist a positive relationship between perceived Static

Budget and perceived Learning

Effectiveness such that a unit increase in perceived Static Budget scores induced about .195-unit rise in perceived Learning Effectiveness scores which was statistically significant at 5 per cent going by the p value (0.001). Based on the result, the null hypothesis is

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rejected; thus Static Budget affected Learning Effectiveness.

Table 6 Coefficientsa Model Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta 1 (Constant) 2.523 .303 8.328 .000 OPERATION BUDGET .212 .073 .218 2.888 .004 STATIC BUDGET .195 .056 .265 3.466 .001

a. Dependent Variable: Learning Effectiveness

Source:

Author’s Fieldwork Computation, 2018

Test of Homoscedasticity and Linearity for Hypothesis Two

From the output below, there appears to be a moderate, positive correlation among the variables. Respondents that are highly affected by Operation Budget and Static Budget experience low levels of Competitive Advantage. On the other hand, firms that are less affected by

Operation Budget and Static Budget have high levels of Competitive Advantage. There is no indication of a curvilinear relationship (test of linearity) and the scatter plot shows a fairly even cigar shape along its length (test of Homoscedasticity). See Fig 5 and 6 respectively.

Fig 5: Scatter Plot of Operation Budget scores and Competitive Advantage Scores

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Fig 6: Scatter Plot of Static Budget scores and Competitive Advantage Scores

Test for Hypothesis Two

Ho2: Operation Budget and Static

Budget does not significantly affect

Competitive Advantage. Standard

multiple regression was adopted to investigate the effects of Operation

Budget and Static Budget on

Competitive Advantage. Preliminary analyses were done to ensure no

contravention of the assumptions of

normality, Multicollinearity,

homoscedasticity and linearity. The result of regression as contained in Table 7: ANOVA, shows that the F-test was 3.828, significant at 5 percent [p<.011]. This showed that the model was well specified

Table 7 ANOVAa Model Sum of Squares Df Mean Squar e F Sig. 1 Regression 3.516 3 1.172 3.828 .011b Residual 59.702 195 .306 Total 63.217 198

a. Dependent Variable: Competitive Advantage b. Predictors: (Constant), Static Budget, Operation Budget

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Also, the result of regression as contained in Table 8: Model Summary, shows that the R Square gave a value of 5.6 per cent. This means that the model

(which includes Operation Budget and Static Budget) explained about 5.6 per cent of the variance in perceived Competitive Advantage.

Table 8 Model Summary Mod el R R Square Adjusted R Square Std. Error of the Estimate 1 .236a _.056 _.041 _.55332

a. Predictors: (Constant), Static Budget, Operation Budget,

Specifically, the result of regression as contained in Table 9 Regression Coefficients, tests the third hypothesis of this study. From the output below, there was no positive relationship between perceived Operation Budget and perceived Competitive Advantage such that a unit increase in Operation Budget scores caused about .214-unit fall in perceived Competitive Advantage scores which was statistically not significant at 5 per cent with the aid of the p value (0.069). Based on the result, the null hypothesis is accepted; thus,

Operation Budget did not affect Competitive Advantage.

Finally, there was negative relationship between perceived Static Budget and perceived Competitive Advantage such that a unit rise in perceived Static Budget scores induced about .115-unit fall in perceived Competitive Advantage scores which is statistically not significant at 5 per cent going by the p value (0.084). Based the result, the null hypothesis is accepted; thus, there was no relationship between Static Budget and Competitive Advantage Table 7 ANOVAa Model Sum of Squares Df Mean Squar e F Sig. 1 Regression 3.516 3 1.172 3.828 .011b Residual 59.702 195 .306 Total 63.217 198

a. Dependent Variable: Competitive Advantage b. Predictors: (Constant), Static Budget, Operation Budget

Also, the result of regression as contained in Table 8: Model Summary, shows that the R Square gave a value of 5.6 per cent. This means that the model

(which includes Operation Budget and Static Budget) explained about 5.6 per cent of the variance in perceived Competitive Advantage.

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Table 8 Model Summary Mod el R R Square Adjusted R Square Std. Error of the Estimate 1 .236a _.056 _.041 _.55332

a. Predictors: (Constant), Static Budget, Operation Budget,

Specifically, the result of regression as contained in Table 9 Regression Coefficients, tests the third hypothesis of this study. From the output below, there was no positive relationship between perceived Operation Budget and perceived Competitive Advantage such that a unit increase in Operation Budget scores caused about .214-unit fall in perceived Competitive Advantage scores which was statistically not significant at 5 per cent with the aid of the p value (0.069). Based on the result, the null hypothesis is accepted; thus,

Operation Budget did not affect Competitive Advantage.

Finally, there was negative relationship between perceived Static Budget and perceived Competitive Advantage such that a unit rise in perceived Static Budget scores induced about .115-unit fall in perceived Competitive Advantage scores which is statistically not significant at 5 per cent going by the p value (0.084). Based the result, the null hypothesis is accepted; thus, there was no relationship between Static Budget and Competitive Advantage Table 9 Coefficientsa Model Unstandardized Coefficients Standardized Coefficients T Sig. B Std. Error Beta 1 (Constant) 4.274 .481 8.892 .000 OPERATION BUDGET -.214 .116 -.150 -1.842 .067 STATIC BUDGET -.155 .089 -.143 -1.735 .084 a. Dependent Variable: Competitive Advantage

Discussion of Findings of Hypothesis One

The findings of this research have shown a positive relationship between

operations budget and learning

effectiveness such that learning

effectiveness is affected by operations budget. Operation budget is the annual budget of an activity stated in terms of budget classification code, functional

categories and cost accounts. It contains estimates of the total value of resources

required for the performance of

operations (Myers, 2004). In

conclusion, the findings have shown that operation budget affected the degree at which learning outcomes is being achieved and the effectiveness of learning programs adopted by the organization. In other words, this

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research finding is tangential to past

findings of scholars that have

discovered that operation budget has the

tendencies to affect learning

effectiveness.

Discussion of Findings of Hypothesis Two

The outcome of this study is in consonance with the views of Owens (2006) which emphasizes the fact whether organizations cut down training budget or maintains a static budget, they still sponsor programs that are essential to recession and prepare for economic recovery which in turns does not affect their competitive advantage. Based on this findings, there is no relationship between static budget and competitive

advantage that is whether the

organizations increase or decrease the

amount spent on learning and

development or whether they maintain the same training budget as in the previous year, it does not affect affects

the organizations competitive

advantage. In conclusion, this research

finding resonates with previous

researches have discovered that there is no relationship between static budget and competitive advantage.

Empirical Findings from the Study

i. This research realized that there is

a significant relationship between operation budget and learning

effectiveness which is in

consonance to past research by Kraiger et al. (2004) where he discovered that learning should be accountable like other investments in order for it to be regarded as an investment. As a result, employers

neglect the training programs completely and this affect learning effectiveness. In other to ensure

learning effectiveness, Shittu

(2012) posited that apart from the workshop and seminar organized by organizations, employees are in need of other attributes which employers emphasize, such as good

personal and social skills,

analytical skills, good

communication skills, technical and managerial skills, etc.

ii. In consonance with the findings of

Owens (2006) which explain the fact that whether organizations reduce or maintain their training

budget they still engage in

programs that will boost their competitive advantage. Based on this result, adopting a static budget

does not affect competitive

advantage that is whether the organizations increase or reduce the money spent on learning and development or whether they maintain the same training budget as in the previous year, it doesn’t

affect the firms competitive

advantage.

Conclusion and Recommendations Today, many organizations are facing a major issue in offering high quality

learning and development in an

environment governed by limited

resources in terms of budget,

equipment, qualified manpower and learning time. Cost effective and efficient solutions are to be found in order to overcome the tight situations. This research concludes that economic meltdown has not too many effect on

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learning and development outcomes of human capital in Nigeria. The study also

concludes that learning and

development outcomes is influenced by the level of economic recession that is existent at the period. Finally, it is being said that economic forces are squeezing growth potential but HR can unlock a prosperous future and this leads to the following recommendations for firms as thus;

i. The findings have established the

significance of learning and

development amongst

entrepreneurial firms. It is therefore required that entrepreneurial firms should not only establish their businesses but they should also invest in their employees learning and development. In addition, the global competitiveness in the economy hinges on effectively and efficiently training of employees

that would culminate in favorable consequences.

ii. The result of this study have shown

the importance of learning cum

development outcomes in a

recessive economy and examining how it relates to human resource

professional. Human resource

professionals in organizations are expected to air the views of employees to the board of directors

as regards learning and

development in other to meet with the world best practice in human

resources. Human resource

professionals in top organizations should also ensure employees are trained from time to time, ensure the required training are the ones given to the employees and highlights the result of employees training and development to the top management.

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